Displaying the steering response with uncertainty in the heatmap ellipse.

Abstract

A computer-implemented method includes receiving a steering command that identifies a toolface orientation, wherein the steering command is expected to produce a anticipated steering response of a anticipated drilling trajectory. The method also includes receiving an actual steering response result of the steering command, wherein the actual steering response result identifies an actual drilling trajectory. The method further includes storing a dataset comparing the actual steering response result with the anticipated steering response, determining an uncertainty level of the toolface orientation based on the stored dataset, and outputting a visual representation of the steering response with the uncertainty level.

Description

[0001] Cross-reference paragraphs

[0002] This application claims the benefit of U.S. nonprovisional application No. 16 / 569,576, filed September 12, 2019, entitled “DISPLAYING STEERING RESPONSE WITHUNCERTAINTY IN A HEAT MAP ELLIPSE”, the disclosure of which is incorporated herein by reference. Background Technology

[0003] In directional drilling projects (e.g., for drilling wells), the orientation of the drilling rig (e.g., the "tool face") is periodically adjusted to drill a hole with a subsurface path along a planned trajectory. The planned trajectory may be relatively straight and vertical in the initial portion, but may become curved and gradually level out at lower depths. Trajectories can be planned and designed to account for various subsurface properties, obstacles, etc., and to maximize oil and gas recovery. Summary of the Invention

[0004] Embodiments of this disclosure may provide a computer-implemented method including receiving a steering command that identifies a toolface orientation, wherein the steering command is expected to produce an expected steering response of an expected drilling trajectory. The method further includes receiving an actual steering response result of the steering command, wherein the actual steering response result identifies an actual drilling trajectory. The method also includes storing a dataset comparing the actual steering response result with the expected steering response, determining an uncertainty level of the toolface orientation based on the stored dataset, and outputting a visual representation of the steering response having the uncertainty level.

[0005] Embodiments of this disclosure may also provide a computing system including one or more processors and a memory system including one or more non-transitory computer-readable media containing one or more stored instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations. The operations may include receiving a drilling trajectory plan that identifies a planned drilling trajectory; determining a tool face orientation for drilling a portion of the planned drilling trajectory by a drilling rig; identifying an uncertainty level associated with the tool face orientation based on steering response uncertainty data stored in a repository; determining a drilling plan for drilling along that portion of the well based on the uncertainty level; and outputting a drilling plan for executing the drilling plan by the drilling rig.

[0006] Embodiments of this disclosure may also provide a non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations. The operations may include determining a plurality of uncertainty levels for a corresponding tool face orientation based on the deviation between a anticipated drilling trajectory and an actual drilling trajectory, storing the plurality of uncertainty levels in a storage library, and outputting a visual representation of the plurality of uncertainty levels.

[0007] It should be understood that this overview is intended only to introduce some aspects of the methods, systems, and media, which are described and / or claimed in more full below. Therefore, this overview is not intended to be limiting. Attached Figure Description

[0008] The accompanying drawings, which are included in and form a part of this specification, illustrate embodiments of the teachings and, together with the specification, serve to explain the principles of the teachings. In the drawings:

[0009] Figure 1 An example of a system according to one embodiment is shown, which includes various management components for managing various aspects of the geological environment.

[0010] Figure 2 An example interface showing a planned drilling trajectory in relation to the actual drilling trajectory is shown according to one embodiment.

[0011] Figure 3 An example drilling control environment according to one embodiment is shown.

[0012] Figure 4 An example steering response ellipse according to one embodiment is shown, where uncertainty data is represented by a heatmap.

[0013] Figure 5 Another example of a steering response ellipse according to one embodiment is shown, where uncertainty data is represented by a heatmap.

[0014] Figure 6 An example steering response ellipse with a varying uncertainty band is shown according to one embodiment.

[0015] Figure 7 An example flowchart is shown, illustrating a process for generating, updating, and presenting a steering response map according to one embodiment, which is used for advance and / or real-time planning of drilling operations in a directional drilling project.

[0016] Figure 8 An example flowchart is shown, illustrating a process for minimizing the deviation between the actual drilling trajectory and the planned drilling trajectory using uncertainty data according to one embodiment.

[0017] Figure 9A schematic diagram of a computing system according to one embodiment is shown. Detailed Implementation

[0018] Effective directional drilling involves adjusting the toolface and / or other operating parameters of the drilling rig to match the actual drilling trajectory of the well (e.g., the wellbore) with the planned trajectory. However, due to various geological factors, the actual trajectory may not follow the planned trajectory. Therefore, aspects of this disclosure can track various data points and analyze data to determine the uncertainty of the steering response and drilling rig trajectory for different toolface orientations and / or based on other conditions. As described herein, “uncertainty” can refer to a quantitative measurement, confidence level, or probability, at least in part, of the actual drilling trajectory matching the expected or desired trajectory based on the toolface orientation. In some embodiments, uncertainty can be based on the number of data points tracking the steering response at a particular toolface orientation and the consistency between the actual and expected steering responses. For example, if a large number of data points (e.g., greater than a threshold number) have been collected when the toolface orientation is set to a particular orientation (e.g., 10 degrees), and the actual and expected steering responses have a consistent low deviation (e.g., below a threshold level), the uncertainty value may be relatively low, indicating a low degree of uncertainty (i.e., a high degree of certainty) in the steering response when the toolface orientation is set to the particular orientation.

[0019] Information regarding steering response and uncertainty can be provided to drilling operators and planners to help plan directional drilling projects and / or adjust drilling equipment parameters in real time to correct or reduce deviations between the actual drilling trajectory and the planned trajectory. Examples of drilling equipment parameters that can be planned and / or adjusted may include equipment drilling speed, torque, power, tool face or drilling direction, and / or any other type of parameter and / or operation used for drilling. Generally, when uncertainty is relatively low, the equipment can be set to drill at higher speeds and torques with less frequent trajectory checks, as these higher speeds and torques are less likely to cause the actual drilling trajectory to deviate from the planned trajectory. Similarly, when uncertainty is relatively high, the equipment can be set to drill at lower speeds and torques with more frequent trajectory checks, as the actual drilling trajectory is more likely to deviate from the planned trajectory.

[0020] As described herein, steering response uncertainty data, relative to the actual drilling trajectory and the planned drilling trajectory, can be presented in a format that is easy to view, synthesize, understand, and apply to improve the effectiveness and accuracy of drilling operations. In some embodiments, aspects of this disclosure can combine heatmaps and ellipses to graphically present steering response and uncertainty in a single view, allowing users to visualize the uncertainty of steering response at different toolface orientations. For example, an elliptic plot can display a steering response ellipse whose uncertainty is based on toolface orientation and turning rate as a function of build rate. Using graphical presentation, operators and / or drilling planners can better visualize uncertainty and make smarter and more effective decisions for planning directional drilling projects in advance and for adjusting drilling plans and operations in real time. Additionally or alternatively, computer-based equipment control devices can automatically adjust equipment operations using steering response and uncertainty data.

[0021] As an illustrative example, aspects of this disclosure can identify and graphically present information indicating relatively low steering response uncertainty in a particular tool face direction. This implies that the steering response is relatively predictable, and the drilling rig is likely to follow an expected trajectory. Based on lower uncertainty, the drilling rig can be set to operate at relatively high speeds and higher torques with fewer adjustment checks. Other drilling operating parameters can be adjusted accordingly. Similarly, when the steering response is relatively high, this means the steering response may be unpredictable, and the drilling rig can be set to operate at lower speeds, lower torques, and with additional adjustment checks.

[0022] As described herein, the planned or anticipated drilling trajectory of a directional drilling project may be relatively straight and vertical in the initial section, but may curve horizontally and gradually straighten at lower depths. The planned trajectory may be divided into multiple segments, and the steering response uncertainty at each segment can be determined (e.g., based on the trajectory of that segment and the corresponding tool face orientation for drilling along the trajectory of that segment). Based on the uncertainty, operating parameters for each segment can be planned. Furthermore, the drilling trajectory can be tracked in real time against the planned trajectory for each segment. If the actual trajectory deviates from the planned trajectory by more than a threshold within a segment, adjustments can be made using uncertainty data to redirect the drilling equipment back toward the planned trajectory. In some embodiments, operators and / or directional drilling planners may use graphical representations of steering response and uncertainty to pre-plan directional drilling projects or to make real-time adjustments during on-site directional drilling projects. Additionally or alternatively, computer-based equipment controls may automatically adjust equipment operation using steering response and uncertainty data.

[0023] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. Numerous specific details are set forth in the following detailed description in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention may be practiced without these specific details. In other instances, well-known methods, processes, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure various aspects of the embodiments.

[0024] It should also be understood that although the terms first, second, etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, without departing from the scope of this disclosure, a first object or step may be referred to as a second object or step, and similarly, a second object or step may be referred to as a first object or step. The first object or step and the second object or step are both objects or steps, but they are not considered to be the same object or step.

[0025] The terminology used in this description is intended to describe particular embodiments and is not intended to be limiting. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any possible combination of one or more of the associated listed items. It will also be understood that the terms “includes,” “including,” “compries,” and / or “comprising,” when used in this specification, specify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof. Furthermore, as used herein, depending on the context, the term “if” can be interpreted as “when,” “in,” “in response to determination,” or “in response to detection.”

[0026] Now, attention is focused on the processing procedures, methods, techniques, and workflows according to some embodiments. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined and / or the order of some operations may be changed.

[0027] Figure 1An example of system 100 is shown, which includes various management components 110 for managing various aspects of a geological environment 150 (e.g., an environment including a sedimentary basin, reservoir 151, one or more faults 153-1, one or more geological bodies 153-2, etc.). For example, management components 110 may allow direct or indirect management of sensing, drilling, injection, extraction, etc., of the geological environment 150. Furthermore, further information about the geological environment 150 may become available as feedback 160 (e.g., optionally as input to one or more management components 110).

[0028] exist Figure 1 In the example, management component 110 includes seismic data component 112, supplementary information component 114 (e.g., well / logging data), processing component 116, simulation component 120, attribute component 130, analysis / visualization component 142, and workflow component 144. During operation, the seismic data and other information provided by each component 112 and 114 can be input into simulation component 120.

[0029] In an example embodiment, simulation component 120 may depend on entity 122. Entity 122 may include earth entities or geological objects, such as wells, surfaces, bodies, reservoirs, etc. In system 100, entity 122 may include a virtual representation of an actual physical entity reconstructed for simulation purposes. Entity 122 may include entities based on data acquired via sensing, observation, etc. (e.g., seismic data 112 and other information 114). Entities may be characterized by one or more attributes (e.g., a geometric cylindrical mesh entity of an earth model may be characterized by a porosity attribute). Such attributes may represent one or more measurements (e.g., acquired data), calculations, etc.

[0030] In an example embodiment, simulation component 120 may operate in conjunction with a software framework such as an object-based framework. In such a framework, entities may include entities based on predefined classes to facilitate modeling and simulation. A commercially available example of an object-based framework is... The framework (Redmond, Washington) provides a set of extensible object classes. Within the framework, object classes encapsulate reusable code modules and related data structures. Object classes can be used to instantiate objects for use by programs, scripts, etc. For example, a borehole class can define objects used to represent boreholes based on well data.

[0031] exist Figure 1In the example, simulation component 120 can process information to conform to one or more attributes specified by attribute component 130, which may include an attribute library. This processing can occur before the input is passed to simulation component 120 (e.g., consider processing component 116). As an example, simulation component 120 can perform operations on input information based on one or more attributes specified by attribute component 130. In the example embodiment, simulation component 120 can construct one or more models of geological environment 150, which may depend on simulating the behavior of geological environment 150 (e.g., responding to one or more behaviors, whether natural or anthropogenic). Figure 1 In the example, the analysis / visualization component 142 may allow interaction with the model or model-based results (e.g., simulation results, etc.). As an example, the output from the simulation component 120 may be input to one or more other workflows, as indicated by the workflow component 144.

[0032] As an example, simulation component 120 may include one or more features of a simulator, such as the ECLIPSETM reservoir simulator (Schlumberger Limited, Houston, Texas), the INTERSECT™ reservoir simulator (Schlumberger Limited, Houston, Texas), etc. As an example, simulation components, simulators, etc., may include features that implement one or more meshless techniques (e.g., solving one or more equations). As an example, one or more reservoirs may be simulated for one or more enhanced oil recovery techniques (e.g., considering thermal processes such as SAGD).

[0033] In an example embodiment, management component 110 may include, for example, Features of commercially available earthquake simulation software frameworks (Schlumberger Limited, Houston, Texas). The framework provides components that allow for the optimization of exploration and development operations. The framework includes seismic-to-simulation software components that can output information to improve reservoir performance, for example, by increasing the productivity of asset teams. By using such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) can develop collaborative workflows and integrate operations to streamline processes. This framework can be considered both an application and a data-driven application (e.g., inputting data for modeling, simulation, etc.).

[0034] In the example embodiment, various aspects of the management component 110 may include additional components or plugins that operate according to the specifications of the framework environment. For example, as The commercial framework environment sold by Schlumberger Limited (Houston, Texas) allows for the integration of add-ons (or plug-ins) into... In the framework workflow. Framework environment utilization The tools (Microsoft Corporation, Redmond, Washington) provide a stable and user-friendly interface for efficient development. In the example implementation, various components can be implemented as additional components (or plugins) that conform to and operate according to the specifications of the framework environment (e.g., according to the application programming interface (API) specification).

[0035] Figure 1 An example of framework 170 is also shown, which includes a model simulation layer 180, a framework service layer 190, a framework core layer 195, and a module layer 175. Framework 170 may include commercially available... The framework, in which model simulation layer 180 is commercially available. A model-centric software package that carries Framework application. In an example embodiment, Software can be considered a data-driven application. The software may include frameworks for model building and visualization.

[0036] As an example, a framework may include features for implementing one or more mesh generation techniques. For instance, a framework may include an input component for receiving information from seismic data interpretation, and one or more attributes based at least in part on seismic data, well logging data, image data, etc. Such a framework may include a mesh generation component that processes the input information and optionally combines other information to generate a mesh.

[0037] exist Figure 1 In the example, the model simulation layer 180 can provide domain objects 182, act as a data source 184, provide rendering 186, and provide various user interfaces 188. Rendering 186 can provide a graphical environment in which application user interfaces 188 can display their data, while user interfaces 188 can provide a common look and feel for application user interface components.

[0038] As an example, domain object 182 may include entity objects, attribute objects, and optional other objects. Entity objects can be used to geometrically represent wells, surfaces, bodies, reservoirs, etc., while attribute objects can be used to provide attribute values ​​as well as data versioning and display parameters. For example, an entity object can represent a well, where attribute objects provide log information as well as versioning information and display information (e.g., displaying the well as part of the model).

[0039] exist Figure 1 In the example, data can be stored in one or more data sources (or data storage, typically physical data storage devices), which can be located at the same or different physical sites and can be accessed via one or more networks. The model simulation layer 180 can be configured to model projects. Therefore, specific projects can be stored, where the stored project information can include inputs, models, results, and cases. Thus, after completing a modeling session, the user can store the project. Later, the project can be accessed and restored using the model simulation layer 180, which can recreate instances of the relevant domain objects.

[0040] exist Figure 1 In the example, geological environment 150 may include layers (e.g., strata) including reservoir 151 and one or more other features, such as fault 153-1, geological body 153-2, etc. For example, geological environment 150 may be equipped with any of a variety of sensors, detectors, actuators, etc. For example, device 152 may include communication circuitry to receive and transmit information about one or more networks 155. Such information may include information related to downhole device 154, which may be a device for acquiring information, assisting in resource recovery, etc. Other devices 156 may be located remotely from the well site and include sensing, detection, transmission, or other circuitry. Such devices may include storage and communication circuitry to store and communicate data, instructions, etc. As an example, one or more satellites may be provided for communication, data acquisition, and other purposes. For example, Figure 1 The satellite in communication is shown together with a network 155 configurable for communication. Note that the satellite may additionally or alternatively include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric measurements, etc.).

[0041] Figure 1 The geological environment 150 is also shown as optionally including well-related equipment 157 and 158, the well comprising a basic horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation, which may include natural fractures, artificial fractures (e.g., hydraulic fractures), or a combination of natural and artificial fractures. As an example, a well may be drilled for a laterally extended reservoir. In such an example, there may be lateral variations in properties, stresses, etc., where assessment of such variations may aid in planning, operations, etc., to develop the laterally extended reservoir (e.g., via fracturing, injection, extraction, etc.). As an example, equipment 157 and / or 158 may include components, systems, etc., for fracturing, seismic sensing, seismic data analysis, assessment of one or more fractures, etc.

[0042] As described above, system 100 can be used to execute one or more workflows. A workflow can be a process that includes multiple work steps. Work steps can manipulate data, for example, to create new data, update existing data, etc. As an example, one or more inputs can be manipulated to create one or more results, for example, based on one or more algorithms. As an example, the system may include a workflow editor for creating, editing, executing, etc., workflows. In such an example, the workflow editor can provide selection of one or more predefined work steps, one or more custom work steps, etc. As an example, a workflow can be... Workflows that can be implemented in the software, such as manipulating seismic data and seismic attributes. As an example, a workflow could be... The process implemented within the framework. For example, a workflow may include one or more work steps that access modules such as plugins (e.g., external executable code).

[0043] Figure 2 Example interface 200 is shown, which displays the planned drilling trajectory relative to the actual drilling trajectory. For example... Figure 2 As shown, the planned trajectory may be relatively straight and vertical in the initial (e.g., shallower) portion, but may curve and gradually straighten horizontally at deeper depths. During drilling, the actual drilling trajectory may deviate from the planned trajectory. Therefore, aspects of this disclosure can minimize deviations by adjusting drilling operations to account for tool face uncertainties. In some embodiments, the planned trajectory can be divided into segments, and a drilling plan can be determined for each segment. The tool face orientation that should be set to drill in the expected trajectory of a segment can be determined, and uncertainties associated with the tool face orientation can be identified. Based on the level of uncertainty, a drilling plan for that segment can be determined. As described herein, the drilling plan can identify equipment operating speed, operating torque level, build rate, turning rate, trajectory monitoring rate, etc. As an illustrative example, for a tool face orientation with relatively low uncertainty values, the operating speed, operating torque level, build rate, and / or turning rate can be relatively higher than for a tool face orientation with relatively high uncertainty values. As drilling progresses, the actual trajectory can be monitored, and the drilling plan can be adjusted to account for uncertainties. Furthermore, steering responses with uncertain data can be presented in elliptical form (e.g., heatmap ellipses), providing operators and planners with rich datasets to help plan drilling projects in advance or make real-time adjustments.

[0044] Figure 3 An example environment according to aspects of this disclosure is shown. For example... Figure 3 As shown, environment 300 may include equipment control device 310, drilling trajectory tracking device 320, steering response and uncertainty planning device 330, and network 340.

[0045] The equipment control device 310 may include one or more computing devices that control the operation of drilling equipment involved in a directional drilling project. For example, the equipment control device 310 may receive commands to control various drilling equipment operations, such as equipment speed, torque, build-up rate, turning speed, etc. In some embodiments, the equipment control device 310 may receive automated commands from the steering response and uncertainty planning device 330 and / or user input commands from the operator.

[0046] The drilling trajectory tracking device 320 may include one or more sensors, accelerometers, magnetometers, and / or data acquisition devices that collect data related to the drilling trajectory. In some embodiments, the drilling trajectory tracking device 320 may be a component of a measurement while drilling (MWD) system. In some embodiments, the drilling trajectory tracking device 320 may collect trajectory data at periodic intervals defined by the drilling plan and report it to the steering response and uncertainty planning device 330.

[0047] The steering response and uncertainty planning device 330 may include one or more computing devices that determine the steering response of the drilling equipment based on different toolface orientations, and further determine the uncertainty of the response in different toolface orientations. In some embodiments, the steering response and uncertainty planning device 330 may determine the steering response and uncertainty by collecting steering response data from real-time drilling operations over a period of time and / or from drilling operations in a test or controlled environment. The steering response and uncertainty planning device 330 may present the steering response and uncertainty data in the form of elliptical and / or combined heat maps and ellipses, which drilling planners and / or operators may use to plan / adjust drilling operations in advance and / or in real time. In some embodiments, the steering response and uncertainty planning device 330 may automatically determine or adjust the drilling plan based on the steering response and uncertainty data. In some embodiments, the drilling trajectory tracking device 320 and / or the steering response and uncertainty planning device 330 may be implemented in one or more applications to help track and / or adjust the drilling plan.

[0048] Network 340 may include network nodes, such as Figure 3Network node 10. Alternatively or additionally, network 340 may include one or more wired and / or wireless networks. For example, network 340 may include a cellular network (e.g., second-generation (3G) network, third-generation (3G) network, fourth-generation (4G) network, fifth-generation (3G) network, Long Term Evolution (LTE) network, Global System for Mobile Communications (GSM) network, Code Division Multiple Access (CDMA) network, Evolved Data Optimized (EVDO) network, etc.), a Public Land Mobile Network (PLMN), and / or another network. Additionally or alternatively, network 340 may include a Local Area Network (LAN), Wide Area Network (WAN), Metropolitan Area Network (MAN), Public Switched Telephone Network (PSTN), Ad Hoc Network, Hosted Internet Protocol (IP) Network, Virtual Private Network (VPN), Intranet, Internet, fiber-optic network, and / or combinations of these or other types of networks. In embodiments, network 340 may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers.

[0049] Figure 4 An example steering response ellipse is shown, where uncertainty data is represented in a heatmap. (See example...) Figure 4 As shown, ellipse 400 can identify a heatmap showing the tool face (TF) used (e.g., based on the actual TF orientation command received by the equipment control unit 310). As described herein, the TF orientation command can correspond to an expected drilling trajectory. The heatmap can include different colors and / or shadings, which represent the level of uncertainty at a particular tool face orientation, build rate, and turn rate. In some embodiments, a darker shading or red can represent a lower level of uncertainty, although any variation between shading and color may be used to represent different levels of uncertainty. In an illustrative example, with a tool face orientation of 0... 0 , build and turn rates from 0 0 - / 100 feet to 15 0 At 100 feet, the level of uncertainty may be relatively low.

[0050] In some embodiments, ellipse 400 can identify uncertainty bands and offset responses. Uncertainty bands can represent the range of uncertainty for a given toolface orientation, and offset responses can identify the actual drilling trajectory relative to the toolface orientation. For example, a 10-degree toolface orientation can have a 1-degree offset, resulting in a 9-degree trajectory. Example ellipse 400 can illustrate the steering response of a particular type of equipment (e.g., a mud motor). In some embodiments, the display of ellipse 400 can be selected between measured depth and true vertical depth. Using ellipse 400, operators or planners can easily visualize the uncertainty of the drilling trajectory based on toolface orientation.

[0051] Figure 5Another example of a steering response ellipse is shown, where uncertainty data is represented in a heatmap. Example ellipse 500 can illustrate the steering response of a particular type of device, such as a rotary steerable system (RSS). Ellipse 500 can have... Figure 4 It uses a format similar to the ellipse 400 in the image and can display different uncertainty levels of RSS devices operating under different tool face orientations, build rates, and turning rates.

[0052] Figure 6 An example steering response ellipse with varying uncertainty bands is illustrated. More specifically, ellipse 600 illustrates the steering response of a device operating under a particular set of demand conditions, as shown. In some embodiments, toolface orientation commands (e.g., received by device control device 310) can be plotted along ellipse 600. Furthermore, the expected toolface orientation can also be shown. The uncertainty level of different toolface orientations is represented by shading around the toolface orientation ellipse. Figure 6 As shown, the uncertainty band (e.g., the range of uncertainty levels) may vary along the toolface orientation ellipse. In some embodiments, the uncertainty band may be wider at orientations with fewer data points (e.g., data points corresponding to the toolface orientation command). For example, the more data points there are, the narrower the uncertainty band. Furthermore, the more data points with consistent results (e.g., consistent actual trajectory with expected trajectory results), the narrower the uncertainty band. In this way, ellipse 600 can be used to visualize uncertainty under different toolface orientations and a set of demand conditions.

[0053] Figure 7 This is an example flowchart illustrating the process of generating, updating, and presenting steering response diagrams for use in advance and / or real-time planning of drilling operations in directional drilling projects. For example, Figure 7 The steps can be found in Figure 3 Implemented in the environment and used Figure 3 The components are described using reference numerals. This flowchart illustrates the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure.

[0054] like Figure 7As shown, process 700 may include receiving a steering command that identifies the toolface orientation (block 710). For example, steering response and uncertainty planning device 330 may receive a steering command that identifies the toolface orientation (e.g., steering angle or toolface angle) from device control device 310. In some embodiments, the steering command may be received by device control device 310 from an operator and / or via an automation system to control the toolface orientation as part of a directional drilling project (e.g., drilling along a planned trajectory based on the toolface). The steering command may be based on an expected steering response. The expected steering response may refer to an expected drilling angle and / or expected drilling trajectory based on the toolface orientation. In some embodiments, the expected steering response may match the toolface orientation or may differ from the toolface orientation. For example, a 10-degree toolface orientation may have a 1-degree offset such that the expected steering response when the toolface orientation is set to 10 degrees is a 9-degree drilling angle / trajectory. In some embodiments, the steering command may identify parameters other than the toolface orientation, such as build rate and turn rate.

[0055] Process 700 may include receiving actual steering response results (block 720). For example, steering response and uncertainty planning device 330 may receive information identifying actual steering response results from drilling trajectory tracking device 320. In some embodiments, actual steering response results may identify the angle of the hole drilled by the equipment and / or the trajectory of the drilled hole.

[0056] Process 700 may include storing a dataset (box 730) that compares the actual steering response with the expected steering response. For example, the steering response and uncertainty planning device 330 may store information comparing the actual steering response with the expected steering response at the tool face orientation (e.g., actual drilling trajectory or angle and expected drilling trajectory or angle). In some embodiments, the information may be stored in a data structure. Comparing the actual steering response with the expected steering response can identify the level of deviation between the actual and expected responses. In some embodiments, the dataset may be timestampd and may include additional metadata such as the geographic location where drilling occurred, equipment operating parameters at the time of drilling, the type of equipment used for drilling, the type of drilling project, the type of drilling application, etc.

[0057] like Figure 7As shown, process 700 can return to box 710 and boxes 710-730 can be repeated. After each iteration of executing boxes 710-730, an additional dataset can be stored, which may include information comparing the actual and expected steering responses at a given toolface orientation / angle, build rate, and / or turn rate. Boxes 710-730 can be repeated in an unlimited number of iterations. In this way, multiple different datasets can be stored, each including information comparing the actual and expected steering responses at different toolface orientations, build rates, and turn rates. In some embodiments, boxes 710-730 can be implemented in a real drilling operation, where the actual and expected steering response datasets are stored. Alternatively or additionally, boxes 710-730 can be implemented in a controlled or test environment.

[0058] Process 700 may also include determining the uncertainty at the toolface orientation (box 740). For example, the steering response and uncertainty planning apparatus 330 may determine the uncertainty value for a given toolface orientation based on a dataset comparing the actual steering response with the expected steering response (e.g., a dataset created after multiple iterations of process boxes 710-730). In some embodiments, the uncertainty may be based on the amount of dataset for a particular toolface orientation and the consistency between the results and the expected steering response. For example, if a large dataset has been analyzed at a 10-degree toolface orientation (e.g., greater than a threshold number) and the actual and expected steering responses have a consistently low deviation (e.g., below a threshold level), the uncertainty value may be relatively low, indicating that the steering response has low uncertainty (i.e., high certainty) when the toolface orientation is set to 10 degrees. As another illustrative example, if a relatively small dataset has been analyzed in an 80-degree toolface orientation and the actual and expected steering responses have a consistently high deviation, the uncertainty value may be relatively high, indicating that the steering response has high uncertainty (i.e., low certainty) when the toolface orientation is set to 80 degrees. In addition to determining uncertainty based on different toolface orientations, uncertainty values ​​can also be determined based on build rate and / or turning rate. Alternatively or additionally, uncertainty can be determined based on additional variables, such as terrain characteristics, equipment type, equipment condition, etc. In some embodiments, box 740 can be repeated, and uncertainties at different toolface orientations, build rates, turning rates, etc., can be updated as additional datasets are generated according to boxes 710-730. In some embodiments, the uncertainty value for each toolface orientation can be stored in a data structure or repository.

[0059] Process 700 may also include receiving a request for a steering response map (block 750). For example, steering response and uncertainty planning apparatus 330 may receive a request for a steering response map. In some embodiments, the request may include one or more parameters that identify a subset of data from which a steering response map can be generated. Example parameters may include the timeframe in which drilling occurred, the geographical location of the drilling, the type of equipment, the type of borehole application, etc.

[0060] Process 700 may also include generating and outputting a steering response map (box 760). For example, the steering response and uncertainty planning device 330 may generate and output a steering response map in the form of a heatmap ellipse, wherein the steering response map may be based on parameters included in the request. As an illustrative example, the steering response map may present a subset of data collected within a specific time frame, or data associated with a specific geographic location, equipment type, drilling application type, etc. Alternatively or additionally, the steering map may be presented in a definition window for measured depth or true vertical depth. As described herein, steering response maps can help directional drilling planners and operators better plan directional drilling projects in advance, or adjust drilling operations in real time to minimize the deviation between the actual drilling trajectory and the planned drilling trajectory.

[0061] Figure 8 This section illustrates an example flowchart of a process for minimizing the deviation between the actual and planned drilling trajectories using uncertainty data. For example, Figure 8 The box can be Figure 3 Implemented in an environment, and used Figure 3 The components are described using reference numerals. This flowchart illustrates the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure.

[0062] like Figure 8 As shown, process 800 may include a drilling trajectory plan (block 805). For example, steering response and uncertainty planning device 330 may receive a drilling trajectory plan associated with a directional drilling project (e.g., for the wellbore). (The above refers to...) Figure 2 An example of a drilling trajectory plan is described.

[0063] Process 800 may further include dividing the planned trajectory into multiple segments (block 810). For example, the steering response and uncertainty planning device 330 may divide the planned trajectory into multiple segments. In some embodiments, s330 may divide the planned trajectory into multiple segments based on receiving (e.g., from an operator or planner) user input and selection to define the segments. Additionally or alternatively, the steering response and uncertainty planning device 330 may automatically divide the planned trajectory into multiple segments based on previous similar drilling projects. In some embodiments, the automatically generated segments may be confirmed and / or manually adjusted via user input and selection. In some embodiments, each segment may include a drilling angle or curve. That is, different segments may be defined as the drilling angle or curve changes along the planned trajectory.

[0064] Process 800 may also include identifying the toolface orientation for drilling within a desired trajectory in a section (box 815). For example, steering response and uncertainty planning device 330 may identify the toolface orientation that should be set for drilling within a desired trajectory in a section. As described above, the toolface orientation or angle may match the drilling trajectory angle, or the toolface orientation may differ based on a predetermined offset.

[0065] Process 800 may also include identifying uncertainties associated with toolface orientation (box 820). For example, steering response and uncertainty planning device 330 may be based on a data structure or repository of uncertainties stored at different toolface orientations (e.g., as described above regarding...). Figure 7 The process block 740 describes a data structure or repository to determine the uncertainty associated with toolface orientation. Alternatively, the operator may use a steering response plot (e.g., a heatmap ellipse) to identify the uncertainty of toolface orientation.

[0066] Process 800 may further include determining a drilling plan for the section based on uncertainty (box 825). For example, steering response and uncertainty planning device 330 may determine a drilling plan for the section based on uncertainty. In some embodiments, steering response and uncertainty planning device 330 may automatically determine a drilling plan based on a set of criteria that identify the drilling plan based on uncertainty. Additionally or alternatively, steering response and uncertainty planning device 330 may receive a drilling plan from an operator or planner via user input, wherein the plan may be determined using uncertainty data. In some embodiments, the drilling plan may identify equipment operating speed, operating torque level, build rate, turning rate, trajectory monitoring rate, etc. As an illustrative example, for a tool face orientation with relatively low uncertainty values, the operating speed, operating torque level, build rate, and / or turning rate may be relatively higher than for a tool face orientation with relatively high uncertainty values.

[0067] Generally, when uncertainty is relatively low, the equipment can be set to drill at higher speeds and torques with less frequent trajectory checks, because these higher speeds and torques are less likely to cause the actual drilling trajectory to deviate from the planned trajectory. Similarly, when uncertainty is relatively high, the equipment can be set to drill at lower speeds and torques with more frequent trajectory checks, because the actual drilling trajectory is more likely to deviate from the planned trajectory.

[0068] Process 800 may also include outputting a drilling plan for execution (block 860). For example, steering response and uncertainty planning device 330 may output a drilling plan for execution (e.g., to equipment control device 310). Alternatively, in some embodiments, the operator may output a drilling plan for execution to equipment control device 310 without involving steering response and uncertainty planning device 330. In any case, equipment control device 310 may execute the drilling plan to cause the drilling equipment to operate according to the drilling plan (e.g., at a planned speed, torque, build-up rate, turning speed, etc.).

[0069] Process 800 may also include monitoring the real-time drilling trajectory (block 835). For example, the steering response and uncertainty planning device 330 may monitor the real-time drilling trajectory based on information received from the drilling trajectory tracking device 320. More specifically, the steering response and uncertainty planning device 330 may monitor the deviation between the real-time trajectory and the planned trajectory. In some embodiments, the steering response and uncertainty planning device 330 may check or monitor the drilling trajectory at a specific frequency or rate, wherein the monitoring rate may be defined by the drilling plan (e.g., a relatively high monitoring rate may be applied when the uncertainty is relatively high).

[0070] Process 800 may further include determining whether the deviation between the real-time trajectory and the planned trajectory is within a threshold level (block 840). For example, steering response and uncertainty planning device 330 may determine whether the real-time trajectory and the planned trajectory are within a threshold level based on monitoring the real-time trajectory. In some embodiments, the threshold level may be configurable and may be a trade-off between minimizing the deviation between the real-time and planned trajectories and the number of adjustments made to the drilling operation.

[0071] For example, if the deviation is not within a threshold level (box 840 - No), process 800 may further include adjusting the drilling plan (box 845). For example, the steering response and uncertainty planning device 330 can adjust the drilling plan by modifying the tool face orientation to change the real-time trajectory toward the planned trajectory. Furthermore, the adjusted drilling plan can adjust (e.g., reduce) speed, torque, build-up rate, turning speed, monitoring rate, etc. Process 800 can return to box 830, whereby the adjusted drilling plan can be output for execution, and the deviation of the real-time drilling trajectory from the planned trajectory can be monitored (box 835). If the deviation is not within a threshold level, further adjustments can be made.

[0072] On the other hand, if the deviation is within a threshold level (box 840 - Yes), process 800 may include determining whether all segments have been completed (box 850). For example, steering response and uncertainty planning device 330 may determine whether all segments of the drilling project have been completed based on drilling analysis and status information received from drilling trajectory tracking device 320.

[0073] For example, if not all sections have been completed and additional sections need to be drilled (box 850 - No), process 800 can return to box 815, whereby the drilling plan for the next section can be determined and executed based on the tool face direction and uncertainties. On the other hand, if all sections have been completed (box 850 - Yes), no further action can be taken and process 800 can end.

[0074] According to Processes 700 and 800, when planning drilling projects in advance and / or adjusting drilling operations in real time, steering response uncertainty data can be considered to minimize the deviation between the planned and actual drilling trajectories. Furthermore, at the end of a directional drilling project, steering response uncertainty data can be used to analyze different characteristics of the well design and their impact on the steering response, such as the type of steering tool (e.g., motor, RSS, etc.), drill bit, BHA, stabilizer and drill collar position, response in different formation areas, drilling parameters used, dip angle and trajectory, wear rates of different tools, the effects of shock and vibration, and / or other influences.

[0075] As described herein, aspects of this disclosure can be used to graphically represent the uncertainty of steering response in different tool face orientations. In some embodiments, uncertainty data can be used to improve directional drilling planning and real-time drilling operations, making the actual drilling trajectory more closely match the planned or expected drilling trajectory. Furthermore, at the end of a directional drilling project, aspects of this disclosure can be used to analyze different characteristics of the well design and how these characteristics affect the steering response (e.g., response to steering tools and motors, drill bit, stabilizer and drill collar positions, formation zone response, drilling parameters used, dip angle and trajectory, wear rate, shocks and vibrations, etc.).

[0076] In some embodiments, the methods disclosed herein may be performed by a computing system. Figure 9 Examples of such a computing system 900 according to some embodiments are illustrated. The computing system 900 may include a computer or a computer system 901A, which may be a standalone computer system 901A or an arrangement of distributed computer systems. The computer system 901A includes one or more analysis modules 902 configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these different tasks, the analysis modules 902 execute independently or in coordination with one or more processors 904 connected to one or more storage media 906. The processor 904 is also connected to a network interface 907 to allow the computer system 901A to communicate with one or more additional computer systems and / or computing systems, such as 901B, 901C and / or 901D, via a data network 909. (Note that computer systems 901B, 901C and / or 901D may or may not share the same architecture as computer system 901A and may be located in different physical locations. For example, computer systems 901A and 901B may be located in a processing facility while communicating with one or more computer systems, such as 901C and / or 901D, located in one or more data centers and / or in different countries on different continents.)

[0077] The processor may include a microprocessor, a microcontroller, a processor module or subsystem, a programmable integrated circuit, a programmable gate array, or another control or computing device.

[0078] Storage medium 906 can be implemented as one or more computer-readable or machine-readable storage media. Note that, although in Figure 9 In the example embodiments, storage medium 906 is depicted as being within computer system 901A; however, in some embodiments, storage medium 906 may be distributed within and / or across multiple internal and / or external chassis of computing system 901A and / or additional computing systems. Storage medium 906 may include one or more different forms of memory, including semiconductor storage devices such as dynamic or static random access memory (DRAM or SRAM), erasable and programmable read-only memory (EPROM), electrically erasable and programmable read-only memory (EEPROM) and flash memory, magnetic disks (such as fixed disks, floppy disks, and removable disks), other magnetic media (including magnetic tape), optical media such as optical discs (CDs) or digital video discs (DVDs), etc. Disk or other types of optical storage, or other types of storage devices. Note that the instructions discussed above can be provided on a single computer-readable or machine-readable storage medium, or on multiple computer-readable or machine-readable storage media distributed across a large system that may have multiple nodes. Such computer-readable or machine-readable storage media or media are considered part of an article (or manufactured article). An article or manufactured article can refer to any single or multiple manufactured components. One or more storage media may be located in a machine that executes the machine-readable instructions, or at a remote site from which machine-readable instructions can be downloaded via a network for execution.

[0079] In some embodiments, computing system 900 includes one or more steering response uncertainty determination modules 908. In an example of computing system 900, computer system 901A includes a steering response uncertainty determination module 908. In some embodiments, a single steering response uncertainty determination module 908 may be used to perform some aspects of one or more embodiments of the methods disclosed herein. In other embodiments, multiple steering response uncertainty determination modules 908 may be used to perform some aspects of the methods herein.

[0080] It should be understood that computing system 900 is merely an example of a computing system, and computing system 900 may have more or fewer components than shown, and may be combined in ways not shown. Figure 9 The additional components depicted in the example embodiments, and / or the computing system 900 may have Figure 9 The different configurations or arrangements of the components described in the text. Figure 9 The various components shown can be implemented in hardware, software, or a combination of hardware and software, including one or more signal processing and / or application-specific integrated circuits.

[0081] Furthermore, the steps in the processing methods described herein can be implemented by running one or more functional modules in an information processing device such as a general-purpose processor or a dedicated chip such as an ASIC, FPGA, PLD, or other suitable device. These modules, combinations of these modules, and / or their combinations with general-purpose hardware are included within the scope of this disclosure.

[0082] Computational interpretations, models, and / or other interpretive aids can be refined iteratively; this concept applies to the methods discussed herein. This can include the use of feedback loops based on algorithmic execution, such as in computing devices (e.g., computing system 900). Figure 9 At this point, and / or through manual control by the user, the user can determine whether a given step, action, template, model, or set of curves has become sufficiently accurate to assess the subsurface three-dimensional geological structure under consideration.

[0083] For purposes of explanation, the above description has been given with reference to specific embodiments. However, the illustrative discussion above is not intended to exhaustively describe or limit the precise forms disclosed. In view of the above teachings, many modifications and variations are possible. Furthermore, the order in which the elements of the methods described herein are illustrated and described may be rearranged, and / or two or more elements may appear simultaneously. The embodiments were chosen and described in order to best explain the principles of this disclosure and its practical application, thereby enabling others skilled in the art to best utilize the disclosed embodiments, as well as various embodiments with various modifications, to suit a particular intended use.

Claims

1. A computer-implemented method, comprising: Receive a steering command that identifies the tool face orientation, wherein the steering command is expected to produce a desired steering response to drill along the desired drilling trajectory; Receive the actual steering response result of the steering command, where the actual steering response result identifies the actual drilling trajectory; Store a dataset that compares the actual steering response with the expected steering response; The level of uncertainty in toolface orientation is determined based on the stored dataset; Wherein, the uncertainty level represents the confidence level at which the actual steering response matches the expected steering response, wherein the expected drilling trajectory is divided into multiple segments, and the uncertainty level at each segment is determined based on the expected drilling trajectory of that segment and the corresponding tool face orientation for drilling along the trajectory of the expected drilling segment. Output a visual representation of the dataset with a level of uncertainty.

2. The method according to claim 1, wherein, The visual representation includes a steering response ellipse with a heatmap, wherein the heatmap includes an indication of the level of uncertainty in a dataset based on toolface orientation, representing toolface orientation.

3. The method according to claim 1, wherein, The steering command further identifies the build rate and the turning rate, wherein the level of uncertainty is further based on the build rate and the turning rate.

4. The method of claim 1 further includes storing a plurality of datasets, each of which is associated with a corresponding tool face orientation, and comparing a plurality of corresponding actual steering response results with a plurality of corresponding expected steering responses.

5. The method according to claim 4, wherein, Determining the level of uncertainty involves basing it on the number of multiple datasets associated with the toolface orientation and the consistency of deviations between multiple corresponding actual steering response results and multiple corresponding expected steering responses within the multiple datasets associated with the toolface orientation.

6. The method of claim 1, further comprising receiving a drilling plan via user input or automatically generating a drilling plan based on the uncertainty level.

7. The method according to claim 1 further includes adjusting the device operation in real time based on the level of uncertainty.

8. The method according to claim 7, wherein, The device operation includes at least one of the following: Operation speed; Operating torque; Build speed; Turning speed; and Track monitoring rate.

9. The method of claim 7, wherein adjusting the device operation in real time based on the uncertainty level comprises increasing torque, speed, or both in well sections with relatively low uncertainty levels, and decreasing torque, speed, or both in well sections with relatively high uncertainty levels.

10. A computing system, comprising: One or more processors; and A memory system comprising a non-transitory computer-readable medium storing one or more instructions, said instructions causing the computing system to perform operations when executed by at least one of one or more processors, said operations including: The drilling trajectory of the receiving plan; Determine the tool face orientation of the drilling rig to produce the desired steering response, so as to drill along a portion of the planned drilling trajectory; Identify the level of uncertainty that represents the confidence level at which the actual steering response matches the expected steering response; The drilling plan for drilling along this section is determined based on the level of uncertainty, wherein the planned drilling trajectory is divided into multiple segments, and the level of uncertainty at each segment is determined based on the planned drilling trajectory for that segment and the corresponding tool face orientation for drilling along the trajectory of the planned drilling segment; and Output drilling plans for drilling equipment to execute.

11. The computing system according to claim 10, wherein, The drilling plan includes at least one of the following: Operation speed; Operating torque; Build speed; Turning speed; and Track monitoring rate.

12. The computing system according to claim 10, further comprising: After outputting the drilling plan for execution, monitor the real-time trajectory; The deviation between the real-time trajectory and the planned drilling trajectory exceeds a threshold; Adjusting the drilling plan based on determining that the deviation exceeds a threshold, wherein adjusting the drilling plan includes identifying the updated tool face orientation and identifying the level of uncertainty associated with the updated tool face orientation; and Output the adjusted drilling plan for execution.

13. The computing system of claim 10 further includes a visual representation of the level of uncertainty output.

14. The computing system according to claim 13, wherein, The visual representation includes at least one of the following: Turning response ellipse with heatmap; and Steering response ellipse with uncertainty band.

15. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations including: Based on the deviation between the expected drilling trajectory and the actual drilling trajectory, the corresponding multiple uncertainty levels of the steering response at different tool face orientations are determined; Multiple uncertainty levels are stored in a reservoir, where the expected drilling trajectory is divided into multiple segments, and the uncertainty level at each segment is determined based on the expected drilling trajectory for that segment and the corresponding tool face orientation for drilling along the trajectory of the expected drilling segment; and Output visual representations of the corresponding multiple uncertainty levels.

16. The computer-readable medium of claim 15, wherein, The visual representation includes at least one of the following: Turning response ellipse with heatmap; and Steering response ellipse with uncertainty band.

17. The computer-readable medium according to claim 15, wherein, The operation also includes: Receive and identify the drilling trajectory plan of the drilling trajectory; Determine the tool face orientation for drilling along a portion of the planned drilling trajectory; Identify the specific uncertainty level associated with the tool face orientation among the stored multiple uncertainty levels; The drilling plan for drilling along this section is determined based on a specific level of uncertainty. and Output the drilling plan, which is used to execute the drilling plan.

18. The computer-readable medium of claim 17, wherein, The drilling program includes at least one of the following: Operation speed; Operating torque; Build speed; Turning speed; and Track monitoring rate.

19. The computer-readable medium according to claim 15, wherein, Each of the multiple corresponding uncertainty levels represents the confidence level that the actual steering response matches the expected steering response.

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